Article: Predictive analytics to prevent homelessness

Introduction

In June 2016, working with one of the major London Housing Associations, we at Develin used ‘predictive analytics’ to identify which of their tenants was at greatest risk of becoming homeless. The predictions were able to look far enough ahead for ‘turnaround’ help to be provided. This is the story.

It was always going to be difficult

The number of people likely to lose their home was a very small proportion of the overall number of tenancies. The information that might identify them early enough to be of use was therefore very hard to come by.

For example, the root causes may involve a relationship breakdown, illness or the loss of a job. Outward signs of normality may remain in place, so no-one would be any the wiser. Eventually, however, the problems would emerge, e.g. mounting arrears. But it might then be too late to turn things around.

Additional data was needed

Using existing data relating to the tenant (e.g. from Housing, CRM and Finance systems) a predictive algorithm identified a pool of tenants most at risk of becoming homeless. But the pool was too big to be of use.

In reality, not everyone identified was at risk of losing their home. To find the few who were each tenant would need to be contacted to see whether any of the tell-tale indicators were present. This was going to take too much time.

Better predictions were needed. This meant finding more relevant data.

The search for new data

Staff who saw tenants everyday knew the tell-tale indicators of emerging distress. They could scan all the records for a tenant, let’s say Mr Smith, and then form a hunch – ‘yes, he looks ok’ or ‘uh oh, Mr Smith maybe getting into difficulty’. But how do you use data to replace a hunch?

The experts in the field (e.g. the Housing Officers) were asked ‘what data would you collect that would raise the flag about a tenant in time for them to be helped?’

They suggested simple markers to indicate when specific things had been seen, or certain things had been mentioned, either by the tenant or by their family and friends. It worked. When this data was combined with that already held about the tenant it delivered a much smaller pool of people known to be at risk of becoming homeless. With a much smaller pool to play with, intervention effort could now be focused towards just those people in greatest need of assistance.

What next?

Develin are developing the technology to make early predictions of potential homelessness a matter of routine. Field Officers will collect relevant markers through their mobile phones and tablets. This data will be combined with the tenant data already held, and a predictive algorithm will build a short list of those potentially at risk of losing their home. Predictive markers will be collected only when it makes sense to do so e.g. during tenant visits or estate inspections. And it will only take a moment. But if those tenants potentially heading for homelessness can be flagged early enough, it could remove the need for a great deal of work further down the line.

If you are from a Local Authority, or a Housing Association, that wants to be better at predicting homelessness, please get in touch.